def matlab_fn_as_matlab_input_data(self, filename): data = load_json(os.path.join('shrp', filename)) mag, index = two_max(data['x'], int(data['lowerbound']) - 1, int(data['upperbound']) - 1, data['unitlen']) np.testing.assert_array_almost_equal(mag, data['mag']) np.testing.assert_array_almost_equal(index, int(data['index']) - 1)
def test_wavread(self): fn = sound_file_path('beijing_f3_50_a.wav') samples, samples_int, Fs = wavread(fn) expected = load_json( os.path.join('helpers', 'beijing_f3_50_a-wavread-expected')) self.assertEqual(Fs, expected['Fs']) self.assertAllClose(samples, expected['y'], rtol=1e-05, atol=1e-08)
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'shr_pitch_data')) wav_data, wavdata_int, fps = wavread( sound_file_path('beijing_f3_50_a.wav')) shr, f0 = shr_pitch(wav_data, fps, 25, 1, 50, 550, 0.4, 5, 200) np.testing.assert_array_almost_equal(f0, data['F0']) np.testing.assert_array_almost_equal(shr, data['SHR'])
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'toframes_data')) res = toframes(data['input'], data['curpos'].astype(int)-1, int(data['segmentlen']), 'hamm') np.testing.assert_array_almost_equal(res, data['frames'])
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'GetLogSpectrum_data')) interp_amplitude = get_log_spectrum(data['segment'], int(data['fftlen']), int(data['limit']) - 1, data['logf'], data['interp_logf']) np.testing.assert_array_almost_equal(interp_amplitude, data['interp_amplitude'])
def matlab_fn_as_matlab_input_data(self, filename): data = load_json(os.path.join('shrp',filename)) mag, index = two_max(data['x'], int(data['lowerbound'])-1, int(data['upperbound'])-1, data['unitlen']) np.testing.assert_array_almost_equal(mag, data['mag']) np.testing.assert_array_almost_equal(index, int(data['index'])-1)
def test_with_min_max_pitch_not_specified(self): data = load_json(os.path.join('shrp', 'shr_pitch_data')) wav_data, wavdata_int, fps = wavread(sound_file_path('beijing_f3_50_a.wav')) with self.assertRaisesRegex(ValueError, 'none or both of min_pitch, max_pitch must be specified'): shr, f0 = shr_pitch(wav_data, fps, min_pitch=50, datalen=200) with self.assertRaisesRegex(ValueError, 'none or both of min_pitch, max_pitch must be specified'): shr, f0 = shr_pitch(wav_data, fps, max_pitch=550, datalen=200)
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'GetLogSpectrum_data')) interp_amplitude = get_log_spectrum( data['segment'], int(data['fftlen']), int(data['limit']) - 1, data['logf'], data['interp_logf']) np.testing.assert_array_almost_equal(interp_amplitude, data['interp_amplitude'])
def test_second_peak_index(self): data = load_json(os.path.join('shrp', 'twomax_data')) x = data['x'] for i in range(142, 146): # Zap the values in the second peak range to exercise the branch. x[i] = 0.0001 * i mag, index = two_max(x, int(data['lowerbound']) - 1, int(data['upperbound']) - 1, data['unitlen']) np.testing.assert_array_almost_equal(mag, np.append(data['mag'], 0.0145)) np.testing.assert_array_equal(index, [int(data['index']) - 1, 145])
def test_med_smooth_greater_than_zero(self): data = load_json(os.path.join('shrp', 'shrp_data')) with self.assertRaises(NotImplementedError): f0_time, f0_value, shr, f0_candidates = shrp( data['Y'], int(data['Fs']), [int(x) for x in data['F0MinMax']], int(data['frame_length']), int(data['timestep']), data['SHR_Threshold'], data['ceiling'], med_smooth=5, CHECK_VOICING=False)
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'shrp_data')) f0_time, f0_value, shr, f0_candidates = shrp( data['Y'], int(data['Fs']), [int(x) for x in data['F0MinMax']], int(data['frame_length']), int(data['timestep']), data['SHR_Threshold'], data['ceiling'], data['med_smooth'], data['CHECK_VOICING']) np.testing.assert_array_almost_equal(f0_time, data['f0_time']) np.testing.assert_array_almost_equal(f0_value, data['f0_value']) np.testing.assert_array_almost_equal(shr, data['SHR']) np.testing.assert_array_almost_equal(f0_candidates, data['f0_candidates'])
def matlab_fn_as_matlab_input_data(self, filename): data = load_json(os.path.join('shrp', filename)) peak_index, shr, shshift, index = compute_shr( data['log_spectrum'], data['min_bin'], data['startpos'].astype(int) - 1, data['endpos'].astype(int) - 1, int(data['lowerbound']) - 1, int(data['upperbound']) - 1, int(data['N']), int(data['shift_units']), data['SHR_Threshold']) np.testing.assert_array_equal(peak_index, int(data['peak_index']) - 1) np.testing.assert_array_almost_equal(shr, data['SHR']) np.testing.assert_array_almost_equal(shshift, data['shshift']) np.testing.assert_array_almost_equal(index, data['index'] - 1)
def test_second_peak_index(self): data = load_json(os.path.join('shrp', 'twomax_data')) x = data['x'] for i in range(142, 146): # Zap the values in the second peak range to exercise the branch. x[i] = 0.0001*i mag, index = two_max(x, int(data['lowerbound'])-1, int(data['upperbound'])-1, data['unitlen']) np.testing.assert_array_almost_equal(mag, np.append(data['mag'], 0.0145)) np.testing.assert_array_equal(index, [int(data['index'])-1, 145])
def test_med_smooth_greater_than_zero(self): data = load_json(os.path.join('shrp', 'shrp_data')) with self.assertRaises(NotImplementedError): f0_time, f0_value, shr, f0_candidates = shrp( data['Y'], int(data['Fs']), [int(x) for x in data['F0MinMax']], int(data['frame_length']), int(data['timestep']), data['SHR_Threshold'], data['ceiling'], med_smooth=5, CHECK_VOICING=False)
def test_with_min_max_pitch_not_specified(self): data = load_json(os.path.join('shrp', 'shr_pitch_data')) wav_data, wavdata_int, fps = wavread( sound_file_path('beijing_f3_50_a.wav')) with self.assertRaisesRegex( ValueError, 'none or both of min_pitch, max_pitch must be specified'): shr, f0 = shr_pitch(wav_data, fps, min_pitch=50, datalen=200) with self.assertRaisesRegex( ValueError, 'none or both of min_pitch, max_pitch must be specified'): shr, f0 = shr_pitch(wav_data, fps, max_pitch=550, datalen=200)
def test_resample_data_against_matlab(self): # XXX: This test fails because SciPy is using a different algorithm # for resampling than Matlab. When the SciPy and Matlab # resampled data are plotted against each other, they look # reasonably similar, so the fact that this test fails is # probably okay. for fn in wav_fns: t = self.tmpdir() tmp_path = os.path.join(t, os.path.basename(fn)) shutil.copy(fn, tmp_path) s = SoundFile(tmp_path, resample_freq=16000) resample_fn = os.path.splitext(os.path.basename(fn))[0] + '-matlab-resample' data = load_json(os.path.join('soundfile', 'resample', resample_fn)) self.assertEqual(len(s.wavdata_rs), len(data['y_rs'])) self.assertAllClose(s.wavdata_rs, data['y_rs'], rtol=1e-01)
def matlab_fn_as_matlab_input_data(self, filename): data = load_json(os.path.join('shrp', filename)) peak_index, shr, shshift, index = compute_shr( data['log_spectrum'], data['min_bin'], data['startpos'].astype(int)-1, data['endpos'].astype(int)-1, int(data['lowerbound'])-1, int(data['upperbound'])-1, int(data['N']), int(data['shift_units']), data['SHR_Threshold']) np.testing.assert_array_equal(peak_index, int(data['peak_index'])-1) np.testing.assert_array_almost_equal(shr, data['SHR']) np.testing.assert_array_almost_equal(shshift, data['shshift']) np.testing.assert_array_almost_equal(index, data['index']-1)
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'shrp_data')) f0_time, f0_value, shr, f0_candidates = shrp( data['Y'], int(data['Fs']), [int(x) for x in data['F0MinMax']], int(data['frame_length']), int(data['timestep']), data['SHR_Threshold'], data['ceiling'], data['med_smooth'], data['CHECK_VOICING']) np.testing.assert_array_almost_equal(f0_time, data['f0_time']) np.testing.assert_array_almost_equal(f0_value, data['f0_value']) np.testing.assert_array_almost_equal(shr, data['SHR']) np.testing.assert_array_almost_equal(f0_candidates, data['f0_candidates'])
def test_wavread(self): fn = sound_file_path('beijing_f3_50_a.wav') samples, samples_int, Fs = wavread(fn) expected = load_json(os.path.join('helpers', 'beijing_f3_50_a-wavread-expected')) self.assertEqual(Fs, expected['Fs']) self.assertAllClose(samples, expected['y'], rtol=1e-05, atol=1e-08)
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'toframes_data')) res = toframes(data['input'], data['curpos'].astype(int) - 1, int(data['segmentlen']), 'hamm') np.testing.assert_array_almost_equal(res, data['frames'])
def main(wav_dir, fs_rs): """Compare original data vs resampled data for all wav files in wav_dir, where resampling frequency is given in Hz by fs_rs """ # Find all .wav files in test/data directory wav_files = glob.glob(os.path.join(wav_dir, '*.wav')) for wav_file in wav_files: print('Processing wav file {}'.format(wav_file)) # y is data points, fs is sampling frequency y, y_int, fs = wavread(wav_file) # ns is number of samples ns = len(y) period = 1.0 / fs # Time points corresponding to samples t = np.arange(0, ns*period, period) # Resample to 16 kHz period_rs = 1.0 / fs_rs # Number of points in resample ns_rs = np.int_(np.ceil(ns * fs_rs / fs)) # Do resample y_rs, t_rs = resample(y, ns_rs, t) y_h, t_h = resample(y, ns_rs, t, window='hamming') # Number of points to plot n = 1000 # Start plotting from this data point # Choose random starting point s = randint(10000, 35015-n) fig = plt.figure() fig.suptitle(os.path.basename(wav_file)) # Compare original data with resampled ata ax = plt.subplot(2,1,1) ax.plot(t, y, 'b-', markersize=1) ax.plot(t_rs, y_rs, 'ro', markersize=3) ax.set_xlim([s*period, (s+n)*period]) ax.set_ylabel('Amplitude') ax.set_title('Normal resample') # Compare original data with resampling + Hamming window ax = plt.subplot(2,1,2) ax.plot(t, y, 'b-', markersize=1) ax.plot(t_h, y_h, 'ro', markersize=3) ax.set_xlim([s*period, (s+n)*period]) ax.set_ylabel('Amplitude') ax.set_xlabel('Time (s)') ax.set_title('Hamming window') plt.savefig(os.path.splitext(os.path.basename(wav_file))[0] + '.pdf') fn = os.path.splitext(os.path.basename(wav_file))[0] + '-matlab-resample' data = load_json(os.path.join('soundfile', fn)) y_rs_matlab = data['y_rs'] # Compare SciPy resampled data with Matlab resampled data plt.figure() plt.plot(np.linspace(0, period * ns, len(y_rs_matlab)), y_rs_matlab, 'b-', markersize=1) plt.plot(t_rs, y_rs, 'ro', markersize=3) plt.xlim(s*period, (s+n)*period) plt.title(os.path.basename(wav_file) + ' - Matlab comparison') plt.xlabel('Time (s)') plt.ylabel('Amplitude') plt.savefig(os.path.splitext(os.path.basename(wav_file))[0] + '-matlab.pdf')
def test_with_matlab_data(self): data = load_json(os.path.join('shrp', 'shr_pitch_data')) wav_data, wavdata_int, fps = wavread(sound_file_path('beijing_f3_50_a.wav')) shr, f0 = shr_pitch(wav_data, fps, 25, 1, 50, 550, 0.4, 5, 200) np.testing.assert_array_almost_equal(f0, data['F0']) np.testing.assert_array_almost_equal(shr, data['SHR'])
def main(wav_dir, fs_rs): """Compare original data vs resampled data for all wav files in wav_dir, where resampling frequency is given in Hz by fs_rs """ # Find all .wav files in test/data directory wav_files = glob.glob(os.path.join(wav_dir, '*.wav')) for wav_file in wav_files: print('Processing wav file {}'.format(wav_file)) # y is data points, fs is sampling frequency y, y_int, fs = wavread(wav_file) # ns is number of samples ns = len(y) period = 1.0 / fs # Time points corresponding to samples t = np.arange(0, ns * period, period) # Resample to 16 kHz period_rs = 1.0 / fs_rs # Number of points in resample ns_rs = np.int_(np.ceil(ns * fs_rs / fs)) # Do resample y_rs, t_rs = resample(y, ns_rs, t) y_h, t_h = resample(y, ns_rs, t, window='hamming') # Number of points to plot n = 1000 # Start plotting from this data point # Choose random starting point s = randint(10000, 35015 - n) fig = plt.figure() fig.suptitle(os.path.basename(wav_file)) # Compare original data with resampled ata ax = plt.subplot(2, 1, 1) ax.plot(t, y, 'b-', markersize=1) ax.plot(t_rs, y_rs, 'ro', markersize=3) ax.set_xlim([s * period, (s + n) * period]) ax.set_ylabel('Amplitude') ax.set_title('Normal resample') # Compare original data with resampling + Hamming window ax = plt.subplot(2, 1, 2) ax.plot(t, y, 'b-', markersize=1) ax.plot(t_h, y_h, 'ro', markersize=3) ax.set_xlim([s * period, (s + n) * period]) ax.set_ylabel('Amplitude') ax.set_xlabel('Time (s)') ax.set_title('Hamming window') plt.savefig(os.path.splitext(os.path.basename(wav_file))[0] + '.pdf') fn = os.path.splitext( os.path.basename(wav_file))[0] + '-matlab-resample' data = load_json(os.path.join('soundfile', fn)) y_rs_matlab = data['y_rs'] # Compare SciPy resampled data with Matlab resampled data plt.figure() plt.plot(np.linspace(0, period * ns, len(y_rs_matlab)), y_rs_matlab, 'b-', markersize=1) plt.plot(t_rs, y_rs, 'ro', markersize=3) plt.xlim(s * period, (s + n) * period) plt.title(os.path.basename(wav_file) + ' - Matlab comparison') plt.xlabel('Time (s)') plt.ylabel('Amplitude') plt.savefig( os.path.splitext(os.path.basename(wav_file))[0] + '-matlab.pdf')